
"""simple docstring"""


from importlib import import_module

from .logging import get_logger


SCREAMING_SNAKE_CASE			  =  get_logger(__name__)



class 	__a	:






       def __init__(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				Dict=None      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		attrs or []
              if module is not None:
                     for key in module.__dict__:
                            if key in attrs or not key.startswith("__"      ):
                                   setattr(self							,   UpperCAmelCase_							,   getattr(UpperCAmelCase_							,   UpperCAmelCase_      )      )
              UpperCamelCase       			=		module._original_module if isinstance(UpperCAmelCase_							,   _PatchedModuleObj      ) else module





class 	__a	:
       UpperCamelCase_		:       Tuple				=      []






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				Dict=None      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		obj
              UpperCamelCase       			=		target
              UpperCamelCase       			=		new
              UpperCamelCase       			=		target.split("."      )[0]
              UpperCamelCase       			=		{}
              UpperCamelCase       			=		attrs or []






       def __enter__(	self			:				int      )->	Dict:







              """simple docstring"""
              *UpperCamelCase			,							UpperCamelCase       			=		self.target.split("."      )

              # Patch modules:
              # it's used to patch attributes of submodules like "os.path.join";
              # in this case we need to patch "os" and "os.path"

              for i in range(len(UpperCAmelCase_      )      ):
                     try:
                            UpperCamelCase       			=		import_module(".".join(submodules[: i + 1]      )      )
                     except ModuleNotFoundError:
                            continue
                     # We iterate over all the globals in self.obj in case we find "os" or "os.path"
                     for attr in self.obj.__dir__():
                            UpperCamelCase       			=		getattr(self.obj							,   UpperCAmelCase_      )
                            # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
                            # This allows to patch renamed modules like "from os import path as ospath".
                            if obj_attr is submodule or (
                                (isinstance(UpperCAmelCase_							,   _PatchedModuleObj      ) and obj_attr._original_module is submodule)
                            ):
                                   UpperCamelCase       			=		obj_attr
                                   # patch at top level
                                   setattr(self.obj							,   UpperCAmelCase_							,   _PatchedModuleObj(UpperCAmelCase_							,   attrs=self.attrs      )      )
                                   UpperCamelCase       			=		getattr(self.obj							,   UpperCAmelCase_      )
                                   # construct lower levels patches
                                   for key in submodules[i + 1 :]:
                                          setattr(UpperCAmelCase_							,   UpperCAmelCase_							,   _PatchedModuleObj(getattr(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )							,   attrs=self.attrs      )      )
                                          UpperCamelCase       			=		getattr(UpperCAmelCase_							,   UpperCAmelCase_      )
                                   # finally set the target attribute
                                   setattr(UpperCAmelCase_							,   UpperCAmelCase_							,   self.new      )

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

              if submodules:  # if it's an attribute of a submodule like "os.path.join"
                     try:
                            UpperCamelCase       			=		getattr(import_module(".".join(UpperCAmelCase_      )      )							,   UpperCAmelCase_      )
                     except (AttributeError, ModuleNotFoundError):
                            return
                     # We iterate over all the globals in self.obj in case we find "os.path.join"
                     for attr in self.obj.__dir__():
                            # We don't check for the name of the global, but rather if its value *is* "os.path.join".
                            # This allows to patch renamed attributes like "from os.path import join as pjoin".
                            if getattr(self.obj							,   UpperCAmelCase_      ) is attr_value:
                                   UpperCamelCase       			=		getattr(self.obj							,   UpperCAmelCase_      )
                                   setattr(self.obj							,   UpperCAmelCase_							,   self.new      )
              elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
                     UpperCamelCase       			=		globals()["__builtins__"][target_attr]
                     setattr(self.obj							,   UpperCAmelCase_							,   self.new      )
              else:
                     raise RuntimeError(f"Tried to patch attribute {target_attr} instead of a submodule."      )






       def __exit__(	self			:				Optional[Any]							,   *UpperCAmelCase_			:				int      )->	Union[str, Any]:







              """simple docstring"""
              for attr in list(self.original      ):
                     setattr(self.obj							,   UpperCAmelCase_							,   self.original.pop(UpperCAmelCase_      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	str:







              """simple docstring"""
              self.__enter__()
              self._active_patches.append(self      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	List[Any]:







              """simple docstring"""
              try:
                     self._active_patches.remove(self      )
              except ValueError:
                     # If the patch hasn't been started this will fail
                     return None

              return self.__exit__()







"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets



class 	__a	(   _lowerCAmelCase						):






       @slow
       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,   "prajjwal1/bert-tiny"      )
              UpperCamelCase       			=		BertTokenizer.from_pretrained("bert-base-uncased"      )

              UpperCamelCase       			=		bertabert.config.encoder.vocab_size
              UpperCamelCase       			=		tokenizer.sep_token_id
              UpperCamelCase       			=		tokenizer.cls_token_id
              UpperCamelCase       			=		128

              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="train[:1%]"      )
              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="validation[:1%]"      )

              UpperCamelCase       			=		train_dataset.select(range(32      )      )
              UpperCamelCase       			=		val_dataset.select(range(16      )      )

              UpperCamelCase       			=		4

              def _map_to_encoder_decoder_inputs(UpperCAmelCase_			:				Any      ):
                     # Tokenizer will automatically set [BOS] <text> [EOS]
                     UpperCamelCase       			=		tokenizer(batch["article"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=512      )
                     UpperCamelCase       			=		tokenizer(batch["highlights"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=128      )
                     UpperCamelCase       			=		inputs.input_ids
                     UpperCamelCase       			=		inputs.attention_mask

                     UpperCamelCase       			=		outputs.input_ids
                     UpperCamelCase       			=		outputs.input_ids.copy()
                     UpperCamelCase       			=		[
                         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                     ]
                     UpperCamelCase       			=		outputs.attention_mask

                     assert all(len(UpperCAmelCase_      ) == 512 for x in inputs.input_ids      )
                     assert all(len(UpperCAmelCase_      ) == 128 for x in outputs.input_ids      )

                     return batch

              def _compute_metrics(UpperCAmelCase_			:				Any      ):
                     UpperCamelCase       			=		pred.label_ids
                     UpperCamelCase       			=		pred.predictions

                     # all unnecessary tokens are removed
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )

                     UpperCamelCase       			=		sum([int(pred_str[i] == label_str[i]      ) for i in range(len(UpperCAmelCase_      )      )]      ) / len(UpperCAmelCase_      )

                     return {"accuracy": accuracy}

              # map train dataset
              UpperCamelCase       			=		train_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              train_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              # same for validation dataset
              UpperCamelCase       			=		val_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              val_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              UpperCamelCase       			=		self.get_auto_remove_tmp_dir()

              UpperCamelCase       			=		SeqaSeqTrainingArguments(
                  output_dir=UpperCAmelCase_							,   per_device_train_batch_size=UpperCAmelCase_							,   per_device_eval_batch_size=UpperCAmelCase_							,   predict_with_generate=UpperCAmelCase_							,   evaluation_strategy="steps"							,   do_train=UpperCAmelCase_							,   do_eval=UpperCAmelCase_							,   warmup_steps=0							,   eval_steps=2							,   logging_steps=2							,   )

              # instantiate trainer
              UpperCamelCase       			=		SeqaSeqTrainer(
                  model=UpperCAmelCase_							,   args=UpperCAmelCase_							,   compute_metrics=_compute_metrics							,   train_dataset=UpperCAmelCase_							,   eval_dataset=UpperCAmelCase_							,   tokenizer=UpperCAmelCase_							,   )

              # start training
              trainer.train()





